MIT-IBM Watson AI Lab

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Teaching AI agents to ask better questions by playing “Battleship”

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Games, Machine Learning, MIT Schwarzman College of Computing, MIT Siegel Family Quest for Intelligence, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Natural Language Processing, Programming, Research, School of Engineering, School of Science

In 2026, the hype for artificial intelligence agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain […]

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Justin Solomon appointed associate dean of engineering education

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Education, teaching, academics, Electrical engineering and computer science (EECS), Faculty, Institute for Medical Engineering and Science (IMES), Leadership, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, School of Engineering

Justin Solomon, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), has been appointed associate dean of engineering education in the MIT School of Engineering, effective July 1.In this new role, Solomon will focus on advancing innovation in engineering education across the school. He will help shape new pedagogical approaches in

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The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

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The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.IBM and MIT today announced the launch of the MIT-IBM Computing Research Lab, advancing their long-standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial intelligence research, with the

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A faster way to estimate AI power consumption

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Energy, Environment, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Sustainability, Sustainable computing, Technology and society

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.Toward that goal, researchers from MIT and the

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MIT engineers design proteins by their motion, not just their shape

ai, AI (Artificial Intelligence), Artificial Intelligence, Biology, Civil and environmental engineering, Computational biology, Health sciences and technology, Materials science and engineering, Mechanical engineering, MIT-IBM Watson AI Lab, Proteins, Research, School of Engineering, Synthetic biology

Proteins are far more than nutrients we track on a food label. Present in every cell of our bodies, they work like nature’s molecular machines. They walk, stretch, bend, and flex to do their jobs, pumping blood, fighting disease, building tissue, and many other jobs too small for the eye to see. Their power doesn’t

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A better method for identifying overconfident large language models

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.But this method measures self-confidence, and even the most impressive LLM might be confidently

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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Careers, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural Language Processing, Research, Robotics, School of Engineering

The early years of faculty members’ careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers’ studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources. For a group of MIT faculty working with and on artificial

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A better method for planning complex visual tasks

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Computer vision, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robotics, School of Engineering

MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques.Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations

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New method could increase LLM training efficiency

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Energy, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Research, Research Laboratory of Electronics, School of Engineering

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful models are particularly good at challenging tasks like advanced programming and multistep planning.But developing reasoning models demands an enormous amount of computation and energy due to inefficiencies in the training process. While

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Mixing generative AI with physics to create personal items that work in the real world

3-D printing, ai, AI (Artificial Intelligence), Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Fabrication, Human-computer interaction, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Have you ever had an idea for something that looked cool, but wouldn’t work well in practice? When it comes to designing things like decor and personal accessories, generative artificial intelligence (genAI) models can relate. They can produce creative and elaborate 3D designs, but when you try to fabricate such blueprints into real-world objects, they

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